Winter Quarter 2008
Electrical Engineering and Computer Science
Northwestern University
INSTRUCTOR:
Bryan Pardo
office location: 3-323
office phone number: 847 491 7184
office hours: 3:30 – 4:30pm, Monday
REQUIRED TEXTBOOK:
Signals
Sound and Sensation by William M. Hartmann
ACCESS TO MATLAB: Lab assignments will be done in Matlab. Matlab is available in the T-lab,
located at the north end of the connector between Tech and the Ford building.
All of the T-lab boxes are dual-boot Linux/Windows. If all of the machines that
default to Linux are in use, a student simply needs to reboot a Windows
box. Right after the BIOS, the GRUB
bootloader will prompt for the OS to boot. Matlab can then be started buy
typing the following command at the command prompt:
/usr/local/matlab/bin/matlab &
The EECS
tech support people will use the class list and work with the lock shop to give
access to the T-Lab to enrolled students in the class. Christopher Bachmann
will also create accounts for students who do not already have one. Those students will receive an email with
instructions to access their account. There may be some students who do not
remember their password or where it has expired. If that is the case, then they should email
PREREQUISITES: Prior programming experience
sufficient to be able to do laboratory assignments in MATLAB is required.
Completion of the Engineering Analysis ( GEN_ENG 205-2 ) series or EECS 211 or
EECS 231 would demonstrate sufficient experience. A willingness to deal with math is also a
prerequisite.
COURSE GOALS: How do you tell the sound of a clarinet from
the sound of a kazoo? Is this song a waltz or a tango? If your friend likes Yo
La Tengo, would she prefer a CD by the Flaming Lips or Bon Jovi? Can a computer
answer these questions?
Researchers in computational music perception apply signal
processing, psychology, music theory, machine learning, and natural language
processing techniques to auditory user interfaces for human-computer
interaction. Current application areas include vocal interfaces and search
engines for music databases, machine accompaniment of human musicians,
automated music recommendation systems, and tools for music production.
Machine Perception of Music will introduce students to the
field of computational music perception through a combination of lectures,
readings, and lab work in MATLAB. Students will learn basics of how sound and
music are recorded and encoded by computers as .wav and
Grading is straightforward. The total points for all
projects sum to 100. Those receiving 93-100 points receive an A. Those with
90-92 receive an A minus, and so on. All students will have the chance to
earn 5 points of extra credit. This is equivalent to a ˝ letter grade boost. No
other alterations to grades will be made. There is no curve.
Each homework assignment must be handed in
as specified in the particular homework assignment. I am not responsible for
homework left in mailboxes.
*NOTE* Assignments are due at the start of class
on the day specified. Late assignments will not be graded. Thus, it is better to hand
in a partial assignment on time than to receive zero credit for a complete
assignment handed in late.
Attendance is not taken. Lateness is a
disruption to the class. Do your level best not to be late. Late assignments
will not be graded.
Do your own work. Academic dishonesty will
be dealt with as laid out in the student handbook.
|
Week |
Day |
Date |
Topic |
Suggested
|
Assigned
|
Due |
Points |
|
1 |
Mon |
7-Jan |
Intro
to the class |
SSS
Chapters 1 & 2 |
|
|
|
|
1 |
Wed |
9-Jan |
Pure
Tones, Power, Intensity, dB |
SSS
Chapter 3 |
|
|
|
|
2 |
Mon |
14-Jan |
Human
auditory system, Loudness |
SSS
Chapter 4 |
HW 1 |
|
|
|
2 |
Wed |
16-Jan |
Pitch,
Musical Frequency |
SSS
Chapters 6, 11, 12 |
|
|
|
|
3 |
Mon |
21-Jan |
NO
CLASS: MLK DAY |
NO
CLASS: MLK DAY |
NO
CLASS |
|
|
|
3 |
Wed |
23-Jan |
Mathematics
of Fourier Series |
SSS
Chapter 8 |
|
HW 1 |
12 |
|
4 |
Mon |
28-Jan |
Mathematics
of Fourier Series |
|
|
Review
1,2 |
4 |
|
4 |
Wed |
30-Jan |
Spectrograms,
Filters |
|
HW 2 |
|
|
|
5 |
Mon |
4-Feb |
More
on Filters, Chromagrams |
Beat
Tracking Papers |
|
Review
3,4 |
4 |
|
5 |
Wed |
6-Feb |
Autocorrelation |
SSS
Chapters 9, 10 |
|
HW 2 |
15 |
|
6 |
Mon |
11-Feb |
Cepstrograms, Final Projects |
|
Final
Project Proposal |
|
|
|
6 |
Wed |
13-Feb |
Similarity
Measurement, Clustering |
|
HW 3 |
Review
5 |
2 |
|
7 |
Mon |
18-Feb |
Midterm
Review, Final Projects |
|
|
Final
Project Proposal |
3 |
|
7 |
Wed |
20-Feb |
MIDTERM, Final Project |
|
|
MIDTERM |
15 |
|
8 |
Mon |
25-Feb |
Audio
Fingerprinting |
Papers
on Audio Fingerprinting |
|
Meeting
with Professor |
10 |
|
8 |
Wed |
27-Feb |
Melody
Recognition |
Papers
on Melody Recognition |
|
HW3 |
15 |
|
9 |
Mon |
3-Mar |
Instrument
Recognition |
Papers
on Instrument Recognition |
|
|
|
|
9 |
Wed |
5-Mar |
Copyright,
Fair play and the Law |
Papers
on Copyright and the Law |
|
Xtra
Credit Review 1-5 |
(5 extra) |
|
10 |
Mon |
10-Mar |
Audio
Source Separation |
Paper
on Audio Source Separation |
|
|
|
|
10 |
Wed |
12-Mar |
Final
Project Presentations |
|
|
Project
Presentations |
10 |
|
10 |
Fri |
14-Mar |
Final
Project Submissions (11:59pm) |
|
|
Project
Submissions |
10 |
The International
Society of Music Information Retrieval (ISMIR) has many useful papers available
HERE.
Useful files
to help you with Machine Perception of Music lab projects
An MPEG implementation in
MATLAB
A good list of music tools
used by computer music researchers
The MIDI toolbox provides
CLAM is a full-fledged software
framework for research and application development in the Audio and Music
Domain. It offers a conceptual model as well as tools for the analysis,
synthesis and transformation of audio signals.
Elias Pampalk’s MA Toolbox for Matlab:
Implementing Similarity Measures for Audio.
The MATLAB Auditory
Demo of the Speech & Hearing Group in the Computer Science Department
at the
Tools
for dealing with music notation
The NETLAB
toolbox for neural networks and other kinds of machine learning in MATLAB.
Dan Ellis’ Matlab Audio
Processing Examples include MFCCs, LPCs, MP3 readers and more
The University of Iowa Musical
Instrument Samples
CNMAT is the UC Berkeley music tech
lab.
CCRMA is the Stanford computer music lab.
IRCAM is the most famous music technology lab
in
The music
tech group at McGill has
lots of cool projects.
Elaine Chew does music technology
research at USC.
Roger Dannenberg is a music technology
researcher at Carnegie Mellon.
Christopher Raphael
is a music technology researcher at
David Temperley does
automated harmonic analysis of music.
Masataka Goto does cool music
technology stuff.
Nina Kraus is
an auditory psychology researcher at Northwestern.
Beverly
Wright does auditory psychology at Northwestern.
Diana Deutsch is an auditory
psychology researcher at UC San Diego.
Dan Levitin does auditory psychology
at
Richard
Ashley does music cognition at Northwestern.
David Huron does
music cognition at
Audacity is free, open source
software for recording and editing sounds. It is available for Mac OS X,
Microsoft Windows, GNU/Linux, and other operating systems.
This site at
McGill university is good for examples of streaming and source segregation.
A wonderful course on
music content analysis by machine, courtesy of Dan Ellis.
This is a FREE book on digital signal
processing that is quite readable, by the standards of such books….
To hear Shepard’s
Tones click on this:
To see and hear the work of Mark
Bartsch on source separation of audio in music click on the following:
Ever wonder
how a woodwind instrument works? Check out this site.
Want to find
out more about bells and how they work? Check this out.
Find out
more about famous and obscure musicians at www.allmusic.com.
Rabiner,
Lawrence R., A
Tutorial on Hidden Markov Models and Selected Applications in Speech
Recognition
Moore, F.
Richard, Elements
of Computer Music
Oppenheim,
Alan V., and Schafer, Ronald W., Discrete-time
Signal Processing is the textbook used at the
Rabiner,
L.R. and Schafer, R.W., Digital
Processing of Speech Signals
Yost,
William A., Fundamentals
of Hearing: An Introduction